class_docstr: HFLoader differs from all other DatasetLoaders because of how it loads data through the Hugging Face
  datasets API instead of saving any files to the cache.
  
  The config for HFLoader contains two unique parameters, huggingface_dataset_id and huggingface_subsample, that
  identify which dataset and which subsample of that dataset to load in.
docstr: Returns a map of split -> pd.DataFrame for the given HF dataset.
  
  :param hf_id: (str) path to dataset on HuggingFace platform
  :param hf_subsample: (str) name of dataset configuration on HuggingFace platform
docstr_tok: ["Returns","a","map","of","split","-",">","pd.DataFrame","for","the","given","HF","dataset",".",":","param","hf_id",":","(","str",")","path","to","dataset","on","HuggingFace","platform",":","param","hf_subsample",":","(","str",")","name","of","dataset","configuration","on","HuggingFace","platform"]
code: def load_hf_to_dict(
      hf_id: str | None = None, hf_subsample: str | None = None
  ) -> dict[str, pd.DataFrame]:
      """Returns a map of split -> pd.DataFrame for the given HF dataset.
  
      :param hf_id: (str) path to dataset on HuggingFace platform
      :param hf_subsample: (str) name of dataset configuration on HuggingFace platform
      """
      dataset_dict: dict[str, datasets.Dataset] = datasets.load_dataset(
          path=hf_id, name=hf_subsample
      )
      pandas_dict = {}
      for split in dataset_dict:
          # Convert from HF DatasetDict type to a dictionary of pandas dataframes
          pandas_dict[split] = dataset_dict[split].to_pandas()
      return pandas_dict
  
file_path: ludwig/ludwig/datasets/loaders/hugging_face.py
imports: from __future__ import annotations
  import logging
  import datasets
  import pandas
  from ludwig.constants import TEST, TRAIN, VALIDATION
  from ludwig.datasets.loaders.dataset_loader import DatasetLoader
label_desc: Use image node_id 1 for calling the HFLoader obj's underlying member method code with example usage: obj.load_hf_to_dict(hf_id, hf_subsample) and returns: pandas_dict